Deep Learning Based Forward Modeling And Inversion Techniques For Computational Physics Problems

Author : Yinpeng Wang,Qiang Ren
Publisher : CRC Press
Page : 200 pages
File Size : 46,7 Mb
Release : 2023-07-06
Category : Computers
ISBN : 9781000896657

Get Book

Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems by Yinpeng Wang,Qiang Ren Pdf

This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems. Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced. As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.

Author : Mehta, Shilpa,Abougreen, Arij Naser
Publisher : IGI Global
Page : 384 pages
File Size : 54,5 Mb
Release : 2023-08-18
Category : Technology & Engineering
ISBN : 9781668482889

Get Book

Metamaterial Technology and Intelligent Metasurfaces for Wireless Communication Systems by Mehta, Shilpa,Abougreen, Arij Naser Pdf

Metamaterials and metasurfaces are enabling modern 5G/6G wireless systems to achieve high performance while maintaining efficient costs and sizes. In the wireless industry, transmission lines play a fundamental role in the development of guided wave elements, antennas, radio frequency identification (RFID) tags, and sensors whose efficiency may be enhanced using metamaterials. Additionally, a metamaterial absorber can solve the bandwidth issue of the internet of things (IoTs) backhaul network. Metasurfaces are also potential candidates for implementing reconfigurable intelligent surfaces (RISs) due to their special wireless communication capabilities. Metamaterial Technology and Intelligent Metasurfaces for Wireless Communication Systems compiles and promotes metamaterials research and sheds light on how metamaterials and metasurfaces will be used in the 5G era and beyond. Covering topics such as active and passive metamaterials, metasurfaces-inspired antennas, and metamaterials for RFID and sensors, this book is ideal for researchers, students, academicians, and professionals.

Author : Qiang Ren,Su Yan,Atef Z. Elsherbeni
Publisher : John Wiley & Sons
Page : 724 pages
File Size : 40,9 Mb
Release : 2022-11-15
Category : Science
ISBN : 9781119808398

Get Book

Advances in Time-Domain Computational Electromagnetic Methods by Qiang Ren,Su Yan,Atef Z. Elsherbeni Pdf

Advances in Time-Domain Computational Electromagnetic Methods Discover state-of-the-art time domain electromagnetic modeling and simulation algorithms Advances in Time-Domain Computational Electromagnetic Methods delivers a thorough exploration of recent developments in time domain computational methods for solving complex electromagnetic problems. The book discusses the main time domain computational electromagnetics techniques, including finite-difference time domain (FDTD), finite-element time domain (FETD), discontinuous Galerkin time domain (DGTD), time domain integral equation (TDIE), and other methods in electromagnetic, multiphysics modeling and simulation, and antenna designs. The book bridges the gap between academic research and real engineering applications by comprehensively surveying the full picture of current state-of-the-art time domain electromagnetic simulation techniques. Among other topics, it offers readers discussions of automatic load balancing schemes for DG-FETD/SETD methods and convolution quadrature time domain integral equation methods for electromagnetic scattering. Advances in Time-Domain Computational Electromagnetic Methods also includes: Introductions to cylindrical, spherical, and symplectic FDTD, as well as FDTD for metasurfaces with GSTC and FDTD for nonlinear metasurfaces Explorations of FETD for dispersive and nonlinear media and SETD-DDM for periodic/ quasi-periodic arrays Discussions of TDIE, including explicit marching-on-in-time solvers for second-kind time domain integral equations, TD-SIE DDM, and convolution quadrature time domain integral equation methods for electromagnetic scattering Treatments of deep learning, including time domain electromagnetic forward and inverse modeling using a differentiable programming platform Ideal for undergraduate and graduate students studying the design and development of various kinds of communication systems, as well as professionals working in these fields, Advances in Time-Domain Computational Electromagnetic Methods is also an invaluable resource for those taking advanced graduate courses in computational electromagnetic methods and simulation techniques.

Author : Deep Ray,Orazio Pinti,Assad A. Oberai
Publisher : Springer
Page : 0 pages
File Size : 44,6 Mb
Release : 2024-07-01
Category : Computers
ISBN : 3031593448

Get Book

Deep Learning and Computational Physics by Deep Ray,Orazio Pinti,Assad A. Oberai Pdf

The main objective of this book is to introduce a student who is familiar with elementary math concepts to select topics in deep learning. It exploits strong connections between deep learning algorithms and the techniques of computational physics to achieve two important goals. First, it uses concepts from computational physics to develop an understanding of deep learning algorithms. Second, it describes several novel deep learning algorithms for solving challenging problems in computational physics, thereby offering someone who is interested in modeling physical phenomena with a complementary set of tools. It is intended for senior undergraduate and graduate students in science and engineering programs. It is used as a textbook for a course (or a course sequence) for senior-level undergraduate or graduate-level students.

Author : Anuj Karpatne,Ramakrishnan Kannan,Vipin Kumar
Publisher : CRC Press
Page : 520 pages
File Size : 46,5 Mb
Release : 2022-08-15
Category : Business & Economics
ISBN : 9781000598131

Get Book

Knowledge Guided Machine Learning by Anuj Karpatne,Ramakrishnan Kannan,Vipin Kumar Pdf

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

Author : Qiang Ren,Yinpeng Wang,Yongzhong Li,Shutong Qi
Publisher : Springer Nature
Page : 137 pages
File Size : 40,9 Mb
Release : 2021-10-20
Category : Technology & Engineering
ISBN : 9789811662614

Get Book

Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning by Qiang Ren,Yinpeng Wang,Yongzhong Li,Shutong Qi Pdf

This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.

Author : Deep Ray
Publisher : Springer Nature
Page : 160 pages
File Size : 49,8 Mb
Release : 2024-07-01
Category : Electronic
ISBN : 9783031593451

Get Book

Deep Learning and Computational Physics by Deep Ray Pdf

Author : Ayush Bhandari,Achuta Kadambi,Ramesh Raskar
Publisher : MIT Press
Page : 482 pages
File Size : 44,9 Mb
Release : 2022-10-25
Category : Technology & Engineering
ISBN : 9780262368377

Get Book

Computational Imaging by Ayush Bhandari,Achuta Kadambi,Ramesh Raskar Pdf

A comprehensive and up-to-date textbook and reference for computational imaging, which combines vision, graphics, signal processing, and optics. Computational imaging involves the joint design of imaging hardware and computer algorithms to create novel imaging systems with unprecedented capabilities. In recent years such capabilities include cameras that operate at a trillion frames per second, microscopes that can see small viruses long thought to be optically irresolvable, and telescopes that capture images of black holes. This text offers a comprehensive and up-to-date introduction to this rapidly growing field, a convergence of vision, graphics, signal processing, and optics. It can be used as an instructional resource for computer imaging courses and as a reference for professionals. It covers the fundamentals of the field, current research and applications, and light transport techniques. The text first presents an imaging toolkit, including optics, image sensors, and illumination, and a computational toolkit, introducing modeling, mathematical tools, model-based inversion, data-driven inversion techniques, and hybrid inversion techniques. It then examines different modalities of light, focusing on the plenoptic function, which describes degrees of freedom of a light ray. Finally, the text outlines light transport techniques, describing imaging systems that obtain micron-scale 3D shape or optimize for noise-free imaging, optical computing, and non-line-of-sight imaging. Throughout, it discusses the use of computational imaging methods in a range of application areas, including smart phone photography, autonomous driving, and medical imaging. End-of-chapter exercises help put the material in context.

Author : Oluwatobi Adeleke,Sina Karimzadeh,Tien-Chien Jen
Publisher : CRC Press
Page : 377 pages
File Size : 49,5 Mb
Release : 2023-12-15
Category : Technology & Engineering
ISBN : 9781003803119

Get Book

Machine Learning-Based Modelling in Atomic Layer Deposition Processes by Oluwatobi Adeleke,Sina Karimzadeh,Tien-Chien Jen Pdf

While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges. Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD. Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.

Author : J. Bee Bednar
Publisher : SIAM
Page : 472 pages
File Size : 45,5 Mb
Release : 1992-01-01
Category : Science
ISBN : 0898712734

Get Book

Geophysical Inversion by J. Bee Bednar Pdf

This collection of papers on geophysical inversion contains research and survey articles on where the field has been and where it's going, and what is practical and what is not. Topics covered include seismic tomography, migration and inverse scattering.

Author : Maokun Li,Marco Salucci
Publisher : IET
Page : 479 pages
File Size : 40,5 Mb
Release : 2023-04-13
Category : Science
ISBN : 9781839535895

Get Book

Applications of Deep Learning in Electromagnetics by Maokun Li,Marco Salucci Pdf

This book discusses recent advances in the application of deep learning techniques to electromagnetic theory and engineering. The contents represent pioneer applications of deep learning techniques to electromagnetic engineering, where physical principles described by the Maxwell's equations dominate.

Author : Mark Asch
Publisher : SIAM
Page : 857 pages
File Size : 52,8 Mb
Release : 2022-08-04
Category : Mathematics
ISBN : 9781611976977

Get Book

A Toolbox for Digital Twins by Mark Asch Pdf

This book brings together the mathematical and numerical frameworks needed for developing digital twins. Starting from the basics—probability, statistics, numerical methods, optimization, and machine learning—and moving on to data assimilation, inverse problems, and Bayesian uncertainty quantification, the book provides a comprehensive toolbox for digital twins. Emphasis is also placed on the design process, denoted as the “inference cycle,” the aim of which is to propose a global methodology for complex problems. Readers will find guidelines and decision trees to help them choose the right tools for the job; a comprehensive reference section with all recent methods, covering both model-based and data-driven approaches; a vast selection of examples and all accompanying code; and a companion website containing updates, case studies, and extended material. A Toolbox for Digital Twins: From Model-Based to Data-Driven is for researchers and engineers, engineering students, and scientists in any domain where data and models need to be coupled to produce digital twins.

Author : Martin Erdmann,Jonas Glombitza,Gregor Kasieczka,Uwe Klemradt
Publisher : World Scientific
Page : 340 pages
File Size : 40,9 Mb
Release : 2021-06-25
Category : Science
ISBN : 9789811237478

Get Book

Deep Learning For Physics Research by Martin Erdmann,Jonas Glombitza,Gregor Kasieczka,Uwe Klemradt Pdf

A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.

Author : Peter Benner,et al.
Publisher : Walter de Gruyter GmbH & Co KG
Page : 356 pages
File Size : 41,8 Mb
Release : 2020-12-16
Category : Mathematics
ISBN : 9783110671490

Get Book

Snapshot-Based Methods and Algorithms by Peter Benner,et al. Pdf

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science.

Author : Rubin H. Landau,Manuel J. Páez,Cristian C. Bordeianu
Publisher : John Wiley & Sons
Page : 597 pages
File Size : 51,7 Mb
Release : 2024-05-13
Category : Science
ISBN : 9783527414253

Get Book

Computational Physics by Rubin H. Landau,Manuel J. Páez,Cristian C. Bordeianu Pdf

The classic in the field for more than 25 years, now with more emphasis on data science and machine learning Computational physics combines physics, applied mathematics, and computer science in a cutting-edge multidisciplinary approach to solving realistic physical problems. It has become integral to modern physics research because of its capacity to bridge the gap between mathematical theory and real-world system behavior. Computational Physics provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. Its philosophy is rooted in “learning by doing”, assisted by many sample programs in the popular Python programming language. The first third of the book lays the fundamentals of scientific computing, including programming basics, stable algorithms for differentiation and integration, and matrix computing. The latter two-thirds of the textbook cover more advanced topics such linear and nonlinear differential equations, chaos and fractals, Fourier analysis, nonlinear dynamics, and finite difference and finite elements methods. A particular focus in on the applications of these methods for solving realistic physical problems. Readers of the fourth edition of Computational Physics will also find: Brand-new chapters on general relativity and the computational physics of soft matter An exceptionally broad range of topics, from simple matrix manipulations to intricate computations in nonlinear dynamics A whole suite of supplementary material: Python programs, Jupyter notebooks and videos Computational Physics is ideal for students in physics, engineering, materials science, and any subjects drawing on applied physics.